Krishna Sai Vootla, Developer in Bengaluru, Karnataka, India
Krishna is currently unavailable

Krishna Sai Vootla

Machine Learning Developer

Bengaluru, Karnataka, India

Toptal member since October 31, 2019

Bio

Krishna is a GenAI product architect and ML engineer with 7+ years of experience delivering AI solutions across marketing, retail, and analytics. He designs end-to-end GenAI products using LLMs, RAG, embeddings, and agentic workflows. At The Weather Company, he led the development of a weather-aware strategy assistant enabling what-if simulations and automated briefings. Krishna combines business analysis with deep ML and LLMOps expertise to build impactful enterprise AI systems.

Portfolio

The Weather Company
AI Agents, Large Language Models (LLMs), FastAPI, Terminal...
Syngenta - Syngenta AG
Artificial Intelligence (AI), Machine Learning...
Organifi
Python 3, Natural Language Processing (NLP)...

Experience

  • Machine Learning - 7 years
  • Python - 6 years
  • Deep Learning - 5 years
  • Natural Language Processing (NLP) - 4 years
  • LangChain - 2 years
  • AI Chatbots - 2 years
  • Large Language Models (LLMs) - 2 years
  • OpenAI - 1 year

Preferred Environment

PyCharm, Tableau, RStudio, Spyder, Jupyter Notebook, SQL, Machine Learning, Python, Claude Code

The most amazing...

...achievement of mine is winning 3rd prize globally in the Intel ESDC competition held in Shanghai, China.

Work Experience

GenAI Product Architect | Senior Data Scientist

2024 - PRESENT
The Weather Company
  • Produced RAG on AWS Bedrock + OpenSearch by parent-child chunking, hybrid search (BM25+dense) with RRF, Cohere cross-encoder reranking, HyDE, query decomposition, citation grounding. Langfuse + RAGAS evals, semantic caching, token streaming.
  • Architected and led the end-to-end design and delivery of a GenAI marketing assistant using an agentic LLM framework to summarize weather impacts, recommend region-season strategies, simulate what-if scenarios, and generate strategic briefings.
  • Collaborated with product, data science, and engineering teams to align GenAI workflows with business priorities, building reusable LLM components and simulation tools.
  • Led technical strategy/implementation of organization-wide user embeddings platform using temporal graph neural networks (GNN), generating user embeddings for 100+ million users, powering personalization, recommendation systems, and user analytics.
  • Spearheaded the development of an enterprise-wide self-supervised learning pipeline leveraging unlabeled data to enable modeling in label-scarce environments and expanding machine learning (ML) capabilities to previously unmodelable user segments.
  • Led the development of an automated model explainability framework leveraging SHAP, PDP, and ICE plots, establishing ML governance standards across teams that streamlined model validation processes and accelerated model adoption across product teams.
Technologies: AI Agents, Large Language Models (LLMs), FastAPI, Terminal, Bayesian Inference & Modeling, TensorFlow, Bash Script, Git, LangChain, AI Design, Document Parsing, AI Programming, Recommendation Systems, PyTorch, Fine-tuning, FAISS, Pinecone, Modeling, Statistics, Applied Statistics, Statistical Methods, Agentic AI, Agentic Frameworks, Deep Learning, Deep Reinforcement Learning, AI Product Management, Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), Large Language Models (LLMs), Retrieval-augmented Generation (RAG), Natural Language Processing (NLP), Docker, Product Management, TensorFlow, PyTorch, Reinforcement Learning, Machine Learning Operations (MLOps), Anthropic, Vector Databases, Cloud, Claude Code, RAG Architecture, Technical Writing, RAG Systems, Scalable Vector Databases, LangSmith, Amazon ElastiCache, Redis

AI Solution Architect

2025 - 2026
Syngenta - Syngenta AG
  • Led end-to-end improvements to enterprise knowledge-base chat systems across Brazil and US domains, strengthening answer reliability through better retrieval architecture using Agentic orchestration, data alignment, and runtime decisioning.
  • Designed and built RAG observability and traceability systems.
  • Drove production-readiness from development to rollout by implementing robust data synchronization, observability, and safe deployment patterns for high-impact AI-assisted workflows.
Technologies: Artificial Intelligence (AI), Machine Learning, Retrieval-augmented Generation (RAG), Anthropic, Vector Databases, Cloud, Artificial Intelligence as a Service (AIaaS), RAG Architecture, RAG Systems, Scalable Vector Databases, LangSmith

Data Scientist | Full-stack AI Engineer

2020 - 2024
Organifi
  • Built a SAC-based dynamic pricing engine to stop margin loss and expiry waste on perishable SKUs; trained on 18-mo demand with guardrails—pilot raised gross margin and cut stock-outs in targeted cohorts.
  • Deployed a DQN checkout upsell to fix stagnant attach rates; action-masking and DR-OPE ensured UX safety—50/50 A/B increased AOV with no conversion drop and lower offer fatigue.
  • Collaborated with marketing leadership to develop data-driven performance measurement frameworks, optimizing campaign ROAS by 18% and guiding $5+ million budget allocation.
  • Development of company-wide churn analytics, including propensity models, lifecycle triggers, and RFM customer segmentation frameworks, that helped implement targeted retention campaigns, reducing churn from 20% to 14% and increasing customer LTV.
  • Developed advanced forecasting models, including seasonality-adjusted SARIMAX for inventory and revenue prediction, contributing to a 35% reduction in stock-outs and improved supply chain management.
  • Led redesign of company-wide experimentation framework to address critical flaws in metric interactions, preventing $5-7 million phantom revenue. Established org-wide A/B testing standards, guardrails, and authored playbooks adopted across product teams.
  • Built data pipelines in AWS and GCP for a reporting and analytics data warehouse.
  • Contributed to designing a centralized OLAP data warehouse with efficient dimensional modeling and took ownership of ETL processes for custom metrics and complex transformations. Implemented cost optimizations that reduced overall scan costs by 25%.
Technologies: Python 3, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Sentiment Analysis, Data Analytics, BigQuery, Google Cloud Functions, Google Cloud, Tableau, Tableau Desktop Pro, MySQL, Amazon Web Services (AWS), AWS Lambda, Amazon RDS, Data Science, Technical Hiring, Code Review, Interviewing, Source Code Review, SQL, Machine Learning, Python, Deep Learning, Dplyr, AWS Glue, Data Modeling, Snowflake, Databases, Artificial Intelligence (AI), Jupyter, Scikit-learn, Anaconda, Team Leadership, Google Cloud Platform (GCP), Agile, Jira, Clustering, Clustering Algorithms, DBSCAN, Hierarchical Clustering, K-means Clustering, Terminal, Bayesian Inference & Modeling, TensorFlow, Bash Script, Git, AI Design, Document Parsing, AI Programming, Fivetran, Microsoft Power BI, Data Build Tool (dbt), PyTorch, Data Engineering, FAISS, Pinecone, Modeling, Statistics, Applied Statistics, Forecasting, Statistical Methods, Artificial Intelligence (AI), Natural Language Processing (NLP), Docker, TensorFlow, PyTorch, Retail Technology, Deep Reinforcement Learning, Reinforcement Learning, Agentic AI, AI Agents, AI Chatbots, AI Product Management, Cloud, Artificial Intelligence as a Service (AIaaS), Spark, Data Lakes, Databricks

Back-end Data Engineer

2023 - 2023
Multithread
  • Developed an end-to-end ETL pipeline from scratch to load data into a graph database and relational database.
  • Built back-end APIs to create customized email text for reaching out to leads using OpenAI API.
  • Created back-end APIs to manipulate and manage data in the graph database.
Technologies: Python 3, Flask, Neo4j, OpenAI GPT-4 API, Google Cloud Platform (GCP), Docker, Vector Search, Clustering, Clustering Algorithms, DBSCAN, Hierarchical Clustering, K-means Clustering, FastAPI, Terminal, Bayesian Inference & Modeling, TensorFlow, Bash Script, Git, Document Parsing, Back-end Development, Data Engineering, Pinecone, Asynchronous Programming, Deep Learning, Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), Large Language Models (LLMs), Retrieval-augmented Generation (RAG), Natural Language Processing (NLP), Docker, Product Management, TensorFlow, PyTorch, Kubernetes, AI Product Management, Cloud

Analyst

2019 - 2019
JP Morgan Chase & Co
  • Designed and built a next-generation merchant acquisition tool in R Shiny for a credit card business.
  • Provided pricing analysis of credit card business.
  • Built and integrated a minimum revenue model based on customer demographics.
Technologies: Tableau, Python, R, Code Review, Source Code Review, SQL, Machine Learning, Jupyter, Scikit-learn, Artificial Intelligence (AI), Algorithms, Terminal, Bayesian Inference & Modeling, Git, Data Build Tool (dbt), Statistics, Statistical Methods, Artificial Intelligence (AI), Natural Language Processing (NLP), PyTorch, Cloud, Spark

Business Analyst

2018 - 2019
Tredence Analytics
  • Partnered with cross-functional teams to prototype dashboards and deliver actionable insights that shaped roadmap-level retail initiatives.
  • Segmented retail customers based on their shopping behavior using Random Forest.
  • Designed, built, and deployed an end-to-end ML pipeline for customer segmentation using Random Forest, enabling targeted marketing strategies.
  • Conducted marketing performance analysis for a leading US retailer, influencing product campaign decisions and strategic planning.
Technologies: Tableau, R, Python, Interviewing, Source Code Review, Task Analysis, Data Science, SQL, Machine Learning, Jupyter, Anaconda, Artificial Intelligence (AI), Terminal, Git, Data Build Tool (dbt), PyTorch, Modeling, Statistics, Deep Learning, Artificial Intelligence (AI), Natural Language Processing (NLP), TensorFlow, PyTorch

Software Analyst

2017 - 2018
Capgemini
  • Scraped the web for collecting unstructured data present on a website.
  • Created and deployed various executive summary dashboards.
  • Automated data cleaning pipelines to save significant person-hours every week.
Technologies: Python, MySQL, Linux, Task Analysis, Git

Experience

LLM Chatbot for Interacting with Documents

This advanced streamline-based application leverages cutting-edge generative AI technology to provide an immersive, interactive chat experience with PDF documents. Users can upload their PDFs, and the application intelligently analyzes the content, enabling various interactive features that make the information more accessible and engaging.

Users can use this application to achieve the following:
1. AI-powered summarization
2. Interactive Q&A

GitHub Link: https://github.com/krishnasaivootla/ChatWithDocs/blob/main/streamlit_app.py

AnalyticsGPT: LLM-based Data Analysis

AnalyticsGPT is a cutting-edge data analysis tool powered by a state-of-the-art language learning model (LLM), where I designed it to transform the way businesses, researchers, and data enthusiasts interact with their datasets, AnalyticsGPT leverages the advanced capabilities of generative AI to provide deep insights, automate data interpretation, and facilitate an intuitive analysis experience.

At the core of AnalyticsGPT is its LLM, which understands and processes complex data patterns and trends. Users can interact with their data through natural language queries, making data analysis more accessible and less time-consuming. Whether you're looking to identify key trends, predict future patterns, or simply explore your data, AnalyticsGPT offers a user-friendly platform that caters to both seasoned analysts and those new to data science.

GitHub Link: https://github.com/krishnasaivootla/AnalyticsGPT/tree/main

Multi-modal Fully Convolutional Network for Semantic Segmentation

https://github.com/prml615/prml
A fully convolutional network (FCN-32s) trained to semantically segment forest scene images with RGB and nir_color input images.

The project was developed to help unmanned drones in smooth navigation. The model is trained and tested on still images of forest scenes.

I used Intel Edison and Microsoft Kinect for proof of concept and prototype creation.

Smart Medical Network

I worked on a smart medical network for Intel ESDC 2016, Shanghai. The project aimed to create an ecosystem of a medical network that stores the clinical and real-time data of patients for smoother and quicker diagnosis in an emergency.

AI Puppy Influencer

I built an automated short video content creation pipeline that uses AI agents to generate content ideas, build scripts for the reels, and generate images from text and videos from images in ComfyUI hosted in Google Colab using Stable Diffusion and AnimateDiff.

Education

2013 - 2017

Bachelor of Technology Degree in Electrical Engineering

Indian Institute of Technology Gandhinagar - Gandhinagar, India

Certifications

MARCH 2020 - PRESENT

Statistical Learning

Stanford Online

OCTOBER 2019 - PRESENT

Sentiment Analysis in Python

DataCamp

MARCH 2019 - PRESENT

Building Web Applications in R with Shiny: Case Studies

DataCamp

MARCH 2019 - PRESENT

Building Web Applications in R with Shiny

DataCamp

OCTOBER 2018 - PRESENT

CodeChef Certified Data Structure & Algorithms Programme

CodeChef

SEPTEMBER 2018 - PRESENT

Intermediate R

DataCamp

AUGUST 2018 - PRESENT

Data Manipulation in R with dplyr

DataCamp

JULY 2018 - PRESENT

Introduction to R

DataCamp

MARCH 2018 - PRESENT

Python A-Z: Python for Data Science with Real Exercises!

Udemy

MARCH 2018 - PRESENT

SQL - MySQL for Data Analytics & Business Intelligence

Udemy

FEBRUARY 2018 - PRESENT

Structuring Machine Learning Projects

Coursera

Skills

Libraries/APIs

PyTorch, Keras, NumPy, Pandas, Matplotlib, Ggplot2, Scikit-learn, OpenCV, TensorFlow, OpenAI API, Tidyverse, Beautiful Soup, Standard Template Library (STL), SciPy

Tools

Jira, Git, Tableau, Dplyr, Scikit-image, Looker, AWS Glue, Terminal, ComfyUI, Microsoft Power BI, Amazon ElastiCache, PyCharm, Jupyter, Spyder, BigQuery, Tableau Desktop Pro, ChatGPT, Amazon SageMaker, Claude Code

Languages

SQL, Python, Python 3, R, Snowflake, Bash Script, C++, C, Embedded C

Frameworks

Agentic Frameworks, RStudio Shiny, Spark, Microsoft Kinect, Flask, LlamaIndex

Platforms

Google Cloud Platform (GCP), Amazon Web Services (AWS), Docker, LangSmith, RStudio, Linux, Oracle, Arduino, Raspberry Pi, Raspberry Pi 3 GPIO, Jupyter Notebook, Anaconda, AWS Lambda, Azure, Databricks

Paradigms

Agile, Asynchronous Programming

Storage

MySQL, Databases, Data Lakes, Redis, Google Cloud, Google Cloud Storage, Neo4j

Industry Expertise

Applied Statistics

Other

Freelancing, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), Data Science, Artificial Intelligence (AI), Technical Hiring, Code Review, Source Code Review, Large Language Models (LLMs), AI Chatbots, Generative Artificial Intelligence (GenAI), Retrieval-augmented Generation (RAG), AI Agents, FAISS, Pinecone, Reinforcement Learning, Modeling, Statistics, Agentic AI, Deep Reinforcement Learning, Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), Large Language Models (LLMs), Natural Language Processing (NLP), Algorithms, Neural Networks, Deep Neural Networks (DNNs), Data Analytics, Data Reporting, Exploratory Data Analysis, Statistical Data Analysis, Statistical Learning, Statistical Modeling, Analytics, Predictive Analytics, Statistical Analysis, Data Analysis, Artificial Neural Networks (ANN), Interviewing, Task Analysis, Generative Pre-trained Transformers (GPT), Chatbots, Minimum Viable Product (MVP), APIs, Data Modeling, LangChain, Scalable Vector Databases, OpenAI GPT-4 API, Open-source LLMs, Team Leadership, Llama 3, Mistral AI, Prompt Engineering, Multi-agent Systems, Vector Search, Clustering, Clustering Algorithms, DBSCAN, Hierarchical Clustering, K-means Clustering, FastAPI, Bayesian Inference & Modeling, AI Design, Document Parsing, AI Programming, Back-end Development, Recommendation Systems, Fivetran, Data Build Tool (dbt), Data Engineering, Forecasting, Statistical Methods, AI Product Management, Retrieval-augmented Generation (RAG), Product Management, TensorFlow, PyTorch, Retail Technology, Machine Learning Operations (MLOps), Anthropic, Vector Databases, Cloud, Artificial Intelligence as a Service (AIaaS), RAG Architecture, Technical Writing, RAG Systems, Quantitative Analysis, Sentiment Analysis, Google Cloud Functions, Amazon RDS, Chatbot Conversation Design, OpenAI, Stable Diffusion, Image to Video, Text to Video, AI Content Creation, Kling AI, Text to Image AI, Image to Text, Text to Image, Fine-tuning, Docker, Kubernetes, AI Product Management

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.

1

Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring